Pathologic evolution-related Gene Analysis based on both single-cell and bulk transcriptomics in Colorectal Cancer

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

Exosomal-miR-522-3p derived from cancer-associated fibroblasts accelerates tumor metastasis and angiogenesis via repression bone morphogenetic protein 5 in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a gastrointestinal tract malignancy. Exosomes secreted by cancer-associated fibroblasts (CAFs) are reported to participate in tumor progression by delivering noncoding RNA or small proteins. However, the function of exosomal miR-522-3p in CRC remains unclear.

METHODS

CAFs were derived from tumor tissues, and exosomes were identified by western blot or TEM/NTA and originated from CAFs/NFs. The viability, invasion, and migration of HUVECs and CRC cells was examined using MTT, Transwell, and wound healing assays, respectively. The molecular interactions were validated using dual luciferase reporter assay and RIP. Xenograft and lung metastasis mouse models were generated to assess tumor growth and metastasis.

RESULTS

Exosomes extracted from CAFs/NFs showed high expression of CD63, CD81, and TSG101. CAF-derived exosomes significantly increased the viability, angiogenesis, invasion, and migration of HUVECs and CRC cells, thereby aggravating tumor growth, invasion, and angiogenesis in vivo. miR-522-3p was upregulated in CAF-derived exosomes and CRC tissues. Depletion of miR-522-3p reversed the effect of exosomes derived from CAFs in migration, angiogenesis, and invasion of HUVECs and CRC cells. Furthermore, bone morphogenetic protein 5 (BMP5) was identified as a target gene of miR-522-3p, and upregulation of BMP5 reversed the promoting effect of miR-522-3p mimics or CAF-derived exosomes on cell invasion, migration, and angiogenesis of HUVECs and CRC cells.

CONCLUSION

Exosomal miR-522-3p from CAFs promoted the growth and metastasis of CRC through downregulating BMP5, which might provide new strategies for the treatment of CRC.

A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as Transcription Factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.

Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network

Objectives

Genome-wide association studies (GWAS) are performed to study the associations between genetic variants with respect to certain phenotypic traits such as cancer. However, the method that is commonly used in GWAS assumes that certain traits are solely affected by a single mutation. We propose a network analysis method, in which we generate association networks of single-nucleotide polymorphisms (SNPs) that can differentiate case and control groups. We hypothesize that certain phenotypic traits are attributable to mutations in groups of associated SNPs.

Methods

We propose a method based on a network analysis framework to study SNP-SNP interactions related to cancer incidence. We employed logistic regression to measure the significance of all SNP pairs from GWAS for the incidence of colorectal cancer and computed a cancer risk score based on the generated SNP networks.

Results

We demonstrated our method in a dataset from a case-control study of colorectal cancer in the South Sulawesi population. From the GWAS results, 20,094 pairs of 200 SNPs were created. We obtained one cluster containing four pairs of five SNPs that passed the filtering threshold based on their p-values. A locus on chromosome 12 (12:54410007) was found to be strongly connected to the four variants on chromosome 1. A polygenic risk score was computed from the five SNPs, and a significant difference in colorectal cancer risk was obtained between the case and control groups.

Conclusions

Our results demonstrate the applicability of our method to understand SNP-SNP interactions and compute risk scores for various types of cancer.